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<Paper uid="W04-3240">
  <Title>Learning to Classify Email into &amp;quot;Speech Acts&amp;quot;</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> It is often useful to classify email according to the intent of the sender (e.g., &amp;quot;propose a meeting&amp;quot;, &amp;quot;deliver information&amp;quot;). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verb-noun pair taken from a predefined ontology describing typical &amp;quot;email speech acts&amp;quot;. We demonstrate that, although this categorization problem is quite different from &amp;quot;topical&amp;quot; text classification, certain categories of messages can nonetheless be detected with high precision (above 80%) and reasonable recall (above 50%) using existing text-classification learning methods. This result suggests that useful task-tracking tools could be constructed based on automatic classification into this taxonomy.</Paragraph>
  </Section>
class="xml-element"></Paper>